article · 2016

Optimization Algorithm

Augusto Luis Ballardini

Cited by 0Abstract and details only

Abstract

K-Means is one of the most popular clustering algorithms, and it is easy to implement It seeks to minimize the sum of squared errors with an iterative optimization At every iteration, it moves the data centroids toward the closer cluster until no point can move anymore Drawbacks It implements a Hill-climbing procedure Highly dependent on the choice of K Sensitive to initialization: how do we choose the initial partitions? K-means and drawbacks

Cite this paper

APA 7

Ballardini, A. L. (2016). Optimization Algorithm.

MLA 9

Ballardini, Augusto Luis. “Optimization Algorithm.” 2016.

Chicago

Ballardini, Augusto Luis. 2016. “Optimization Algorithm.”

Harvard

Ballardini, A.L. (2016) ‘Optimization Algorithm’.

BibTeX

@article{ballardini2016optimization,
  title = {{Optimization Algorithm}},
  author = {Ballardini, Augusto Luis},
  year = {2016}
}

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